1. Field of the Invention
This invention pertains to a system and method for analyzing financial risk data. More particularly, the present invention relates to system and method for determining a value at risk of a financial portfolio.
2. Description of the Related Art
Modern markets are filled with pitfalls for unwary investment institutions and lenders. A value at risk ("VAR") of a particular financial portfolio is an important consideration for any financial institution engaged in investment and/or lending operations. The VAR of a portfolio indicates the portfolio's market risk. In other words, the VAR is the greatest possible loss that the institution may expect in the portfolio in question with a certain given degree of probability during a certain future period of time. Typically, a financial institution will put aside a certain percentage of the VAR as a contingency amount to cover possible losses in the portfolio in a predetermined upcoming time period. Thus, the accuracy of VAR estimation is of utmost importance since the funds set aside to cover the VAR are not generating any income for the institution. Furthermore, if the estimation of VAR is too low, the institution may expose itself to a highly undesirable situation where it does not have enough funds set aside to cover a portfolio loss.
As a result, two major approaches have been developed to provide an accurate estimation of VAR for a portfolio. For both approaches, a predetermined number of financial data samples are generated using appropriate simulation methods, e.g. Monte Carlo, Historical simulation, etc. The samples are then ordered from largest gain/smallest loss to largest loss or vice versa. First, a certain predetermined probability percentage called confidence level ("CL") is selected by an analyst to define a desired confidence level for VAR estimation. Typically CL is approximately 95% or higher. Then, the set of samples is analyzed to estimate the VAR value. It is assumed that probability CL future losses will not exceed this value with probability CL.
The first approach is called the percentile method. This approach assumes that VAR is equal to the lowest loss of (100-CL) % of highest losses in the set of samples. For example, assuming that CL is set to 95% and there are 100 samples in a set sorted from the smallest loss to the largest loss, and 6 largest losses (in millions) are 65, 65, 95, 95, 97, and 1000. Then, VAR will be $65 million, because there are less than 5% of samples with value of loss that is higher than 65 million. While this approach has the advantage of relative simplicity it is flawed because the VAR value is based on the only one sample value that happened to be on the border of (100-CL) % of samples in the set analyzed. If for example, in the set under consideration only one sample has changed its value and 6 largest losses (in millions) are 65, 88, 95, 95, 97, and 1000, then for the same portfolio, VAR would still be $65 million.
The second approach is similar to the percentile approach except that the losses in the (100-CL) percentile are averaged to obtain the VAR. This approach is also problematic because if even one unusually high loss happened to be among the losses, the value of VAR will be skewed to an undesirably high value because the unusually high loss will affect the average. For example, in the set above, use of this VAR estimation method returns VAR of an unreasonable high value of 256 million.
It would thus be desirable to provide a system and method for accurately determining a VAR for a financial portfolio that is not adversely affected by uneven or skewed distribution of loss samples. It would further be desirable to provide estimation of limits of possible values of VAR so that an analyst or an additional financial data analysis system may estimate the accuracy of VAR value calculated.